Meteorological Service

Service Description Weather Prediction Model Development

Weather information delivered by cars has a unique potential to improve the quality of weather prediction models. The data can be used directly to feed the models with real-time information as well as indirectly in order to improve the physical treatments in the models.

How can meteorological data from cars be useful?

Weather prediction models are used to calculate the weather forecast. The quality of the weather forecast depends strongly on the conditions of the atmosphere (e.g., temperature, humidity, pressure) at the start of the forecast (so called initial conditions). The information about these initial values of meteorological parameters origins mostly from the last available measurements of conventional weather stations. In the already quite dense meteorological measurement network in Germany, the distance between two stations is often more than 20km. Meteologix’ own Swiss HD Model uses a spatial resolution of 1x1km. At present, in order to define the initial conditions for such high resolution models, gaps between the weather stations are filled by making use of earlier forecasts and interpolation methods. Meteorological parameters such as temperature and humidity measured by cars can help to fill the gaps between the conventional stations with more accurate measured values. This may lead to less uncertainty of the initial meteorological conditions and hence improved accuracy of the weather forecast.

In addition, in order to improve the model physics, a weather forecast is evaluated against the actual meteorological measurements it was trying to predict. Again, the higher the spatial data density of measured weather information is, the better it can be analysed where and why a forecast was particularly accurate or inaccurate. Such knowledge is necessary to improve the model physics and to test potential improvements. Finally, these improvements will make the weather prediction models more accurate.

Automat

During Automat, we want to investigate the general applicability of weather data delivered by cars for meteorological purposes. The complete data chain, i.e., in-car measurement of meteorological parameters, data delivery and download, plausibility check, and usage as input for weather forecast models is tested.

Use Case: Comparability of conventional meteorological measurements and data from car sensors

In contrast, to conventional meteorological measurement systems, car sensors are relatively cheap, simple, and are place not according to standard guidelines (e.g., temperature should be measured two metres above ground). Therefore, car sensors are potentially more erroneous and biased. In order to investigate the feasibility of using car sensors in addition to conventional measurements, the two types of observations are compared to each other. At the VW test site Ehra, meteorological stations were installed along the track. The investigations include tests of methods for outlier detection and plausibility checks as well as a quality test of the car sensor data against conventional measurements.

Use Case: Use of Automat car data as input for weather forecast models

The car sensor data that is collected during Automat is used as additional information for the initial conditions of our Swiss HD Model forecasts. The purpose of this Use Case is to test our data assimilation methods with this new type of data. Model forecasts making use of car sensor data are conducted in order to demonstrate the general applicability of weather data delivered by cars for meteorological purposes

Project Coordination & Contact

The work described in this website has been conducted within the project AUTOMAT. This project has received funding from the European Union’s Horizon 2020 (H2020) research and innovation programme under the Grant Agreement no 644657. This website and the content displayed in it do not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of its content.

Project funded through the European Union’s Horizon 2020 research and innovation programme under Grant Agreement Nr. 644657